1. A study on Machine Learning Approaches for Predicting and Analyzing the Drying Process in the Textile Industry
- Author
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Wen-June Wang, Xiang-Yun Deng, Ke-Haur Taur, Mi-Huo Chou, Yi-Hsiu Lee, and Jing-Wei Chen
- Subjects
Process quality ,Textile industry ,Artificial neural network ,Computer science ,business.industry ,Process (computing) ,Conveyor belt ,Machine learning ,computer.software_genre ,Metric (mathematics) ,Factory (object-oriented programming) ,Artificial intelligence ,business ,computer ,Energy (signal processing) - Abstract
The main objective of this paper is to establish an output/input relationship model based on machine learning for the fabric drying process of a general textile factory. The scenario of the fabric drying process involves a conveyor belt that drives the fabric through eight drying boxes, and the targeted metric of the post-drying fabric is the moisture content rate. This paper is composed of two main parts. The first part is to explain that how to select a predictive model measuring the output performance of the setting machine. The second part discusses the optimization of energy-saving parameters of multiple models chosen in the first part. This paper will introduce some techniques such as neural networks and machine learning algorithms to find the most suitable output/input relationship model, or so called “drying process quality prediction model”, for future development of energy saving.
- Published
- 2019
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